




import copy
import os
from typing import Union

from ...configuration_utils import PretrainedConfig
from ...models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
from ...utils import logging
from ..auto import CONFIG_MAPPING


__a							     =					logging.get_logger(__name__)

__a							     =					{
    'Salesforce/instruct-blip-flan-t5': 'https://huggingface.co/Salesforce/instruct-blip-flan-t5/resolve/main/config.json',
}

class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''instructblip_vision_model'''





    def __init__( self  ,_SCREAMING_SNAKE_CASE=1_408  ,_SCREAMING_SNAKE_CASE=6_144  ,_SCREAMING_SNAKE_CASE=39  ,_SCREAMING_SNAKE_CASE=16  ,_SCREAMING_SNAKE_CASE=224  ,_SCREAMING_SNAKE_CASE=14  ,_SCREAMING_SNAKE_CASE="gelu"  ,_SCREAMING_SNAKE_CASE=1e-6  ,_SCREAMING_SNAKE_CASE=0.0  ,_SCREAMING_SNAKE_CASE=1e-10  ,_SCREAMING_SNAKE_CASE=True  ,**_SCREAMING_SNAKE_CASE  ,)					->						Any:
         super().__init__(**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Optional[int]  =     hidden_size
         UpperCAmelCase_     :				List[Any]  =     intermediate_size
         UpperCAmelCase_     :				Tuple  =     num_hidden_layers
         UpperCAmelCase_     :				Dict  =     num_attention_heads
         UpperCAmelCase_     :				Optional[Any]  =     patch_size
         UpperCAmelCase_     :				str  =     image_size
         UpperCAmelCase_     :				Optional[Any]  =     initializer_range
         UpperCAmelCase_     :				Optional[int]  =     attention_dropout
         UpperCAmelCase_     :				List[str]  =     layer_norm_eps
         UpperCAmelCase_     :				List[Any]  =     hidden_act
         UpperCAmelCase_     :				Any  =     qkv_bias





    @classmethod
    def 				a__ ( cls  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						"PretrainedConfig":
         cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_,       UpperCAmelCase_     :				Tuple  =     cls.get_config_dict(_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )

         # get the vision config dict if we are loading from InstructBlipConfig
         if config_dict.get('''model_type'''    ) == "instructblip":
              UpperCAmelCase_     :				Optional[Any]  =     config_dict['''vision_config''']

         if "model_type" in config_dict and hasattr(cls  ,'''model_type'''    ) and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
                  f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''    )

         return cls.from_dict(_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )


class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''instructblip_qformer'''





    def __init__( self  ,_SCREAMING_SNAKE_CASE=30_522  ,_SCREAMING_SNAKE_CASE=768  ,_SCREAMING_SNAKE_CASE=12  ,_SCREAMING_SNAKE_CASE=12  ,_SCREAMING_SNAKE_CASE=3_072  ,_SCREAMING_SNAKE_CASE="gelu"  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=0.1  ,_SCREAMING_SNAKE_CASE=512  ,_SCREAMING_SNAKE_CASE=0.02  ,_SCREAMING_SNAKE_CASE=1e-12  ,_SCREAMING_SNAKE_CASE=0  ,_SCREAMING_SNAKE_CASE="absolute"  ,_SCREAMING_SNAKE_CASE=2  ,_SCREAMING_SNAKE_CASE=1_408  ,**_SCREAMING_SNAKE_CASE  ,)					->						int:
         super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				List[Any]  =     vocab_size
         UpperCAmelCase_     :				Optional[int]  =     hidden_size
         UpperCAmelCase_     :				int  =     num_hidden_layers
         UpperCAmelCase_     :				Any  =     num_attention_heads
         UpperCAmelCase_     :				Optional[int]  =     hidden_act
         UpperCAmelCase_     :				List[Any]  =     intermediate_size
         UpperCAmelCase_     :				Optional[int]  =     hidden_dropout_prob
         UpperCAmelCase_     :				List[Any]  =     attention_probs_dropout_prob
         UpperCAmelCase_     :				List[Any]  =     max_position_embeddings
         UpperCAmelCase_     :				int  =     initializer_range
         UpperCAmelCase_     :				List[str]  =     layer_norm_eps
         UpperCAmelCase_     :				Tuple  =     position_embedding_type
         UpperCAmelCase_     :				int  =     cross_attention_frequency
         UpperCAmelCase_     :				Optional[int]  =     encoder_hidden_size





    @classmethod
    def 				a__ ( cls  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )					->						"PretrainedConfig":
         cls._set_token_in_kwargs(_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_,       UpperCAmelCase_     :				Optional[int]  =     cls.get_config_dict(_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )

         # get the qformer config dict if we are loading from InstructBlipConfig
         if config_dict.get('''model_type'''    ) == "instructblip":
              UpperCAmelCase_     :				str  =     config_dict['''qformer_config''']

         if "model_type" in config_dict and hasattr(cls  ,'''model_type'''    ) and config_dict["model_type"] != cls.model_type:
              logger.warning(
                  f'''You are using a model of type {config_dict["model_type"]} to instantiate a model of type '''
                  f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.'''    )

         return cls.from_dict(_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE    )


class     __a(	_a  ):






    """simple docstring"""





    lowerCAmelCase         = '''instructblip'''
    lowerCAmelCase         = True





    def __init__( self  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=None  ,_SCREAMING_SNAKE_CASE=32  ,**_SCREAMING_SNAKE_CASE    )					->						List[str]:
         super().__init__(**_SCREAMING_SNAKE_CASE    )

         if vision_config is None:
              UpperCAmelCase_     :				List[Any]  =     {}
              logger.info('''vision_config is None. initializing the InstructBlipVisionConfig with default values.'''    )

         if qformer_config is None:
              UpperCAmelCase_     :				Tuple  =     {}
              logger.info('''qformer_config is None. Initializing the InstructBlipQFormerConfig with default values.'''    )

         if text_config is None:
              UpperCAmelCase_     :				str  =     {}
              logger.info('''text_config is None. Initializing the text config with default values (`OPTConfig`).'''    )

         UpperCAmelCase_     :				List[str]  =     InstructBlipVisionConfig(**_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Optional[Any]  =     InstructBlipQFormerConfig(**_SCREAMING_SNAKE_CASE    )
         UpperCAmelCase_     :				Dict  =     text_config['''model_type'''] if '''model_type''' in text_config else '''opt'''
         UpperCAmelCase_     :				List[Any]  =     CONFIG_MAPPING[text_model_type](**_SCREAMING_SNAKE_CASE    )

         UpperCAmelCase_     :				Tuple  =     self.text_config.tie_word_embeddings
         UpperCAmelCase_     :				List[Any]  =     self.text_config.is_encoder_decoder

         UpperCAmelCase_     :				int  =     num_query_tokens
         UpperCAmelCase_     :				Any  =     self.vision_config.hidden_size
         UpperCAmelCase_     :				List[Any]  =     self.text_config.model_type in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
         UpperCAmelCase_     :				Optional[Any]  =     1.0
         UpperCAmelCase_     :				Any  =     0.02





    @classmethod
    def 				a__ ( cls  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,_SCREAMING_SNAKE_CASE  ,**_SCREAMING_SNAKE_CASE  ,)					->						Optional[Any]:
         return cls(
             vision_config=vision_config.to_dict()  ,qformer_config=qformer_config.to_dict()  ,text_config=text_config.to_dict()  ,**_SCREAMING_SNAKE_CASE  ,)





    def 				a__ ( self    )					->						str:
         UpperCAmelCase_     :				Union[str, Any]  =     copy.deepcopy(self.__dict__    )
         UpperCAmelCase_     :				Any  =     self.vision_config.to_dict()
         UpperCAmelCase_     :				List[str]  =     self.qformer_config.to_dict()
         UpperCAmelCase_     :				Dict  =     self.text_config.to_dict()
         UpperCAmelCase_     :				Optional[Any]  =     self.__class__.model_type
         return output







'''simple docstring'''







import argparse

import numpy as np
import torch

from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging


logging.set_verbosity_info()
__lowerCAmelCase        =							logging.get_logger("transformers.models.speecht5")


def       __UpperCamelCase					(      lowercase_       :  Optional[int]   ,       lowercase_       :  Dict   ,       lowercase_       :  Any   ):
       """simple docstring"""




       hf_model.apply_weight_norm()

       a_						     =			checkpoint['input_conv.weight_g']
       a_						     =			checkpoint['input_conv.weight_v']
       a_						     =			checkpoint['input_conv.bias']

       for i in range(len(config.upsample_rates   )   ):
              a_						     =			checkpoint[F'upsamples.{i}.1.weight_g']
              a_						     =			checkpoint[F'upsamples.{i}.1.weight_v']
              a_						     =			checkpoint[F'upsamples.{i}.1.bias']

       for i in range(len(config.upsample_rates   ) * len(config.resblock_kernel_sizes   )   ):
              for j in range(len(config.resblock_dilation_sizes   )   ):
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_g']
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.weight_v']
                     a_						     =			checkpoint[F'blocks.{i}.convs1.{j}.1.bias']

                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_g']
                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.weight_v']
                     a_						     =			checkpoint[F'blocks.{i}.convs2.{j}.1.bias']

       a_						     =			checkpoint['output_conv.1.weight_g']
       a_						     =			checkpoint['output_conv.1.weight_v']
       a_						     =			checkpoint['output_conv.1.bias']

       hf_model.remove_weight_norm()


@torch.no_grad()
def       __UpperCamelCase					(      lowercase_       :  List[Any]   ,       lowercase_       :  int   ,       lowercase_       :  Optional[Any]   ,       lowercase_       :  List[str]=None   ,       lowercase_       :  Tuple=None   ,       ):
       """simple docstring"""




       if config_path is not None:
              a_						     =			SpeechTaHifiGanConfig.from_pretrained(lowercase_   )
       else:
              a_						     =			SpeechTaHifiGanConfig()

       a_						     =			SpeechTaHifiGan(lowercase_   )

       a_						     =			torch.load(lowercase_   )
       load_weights(orig_checkpoint['model']['generator']   ,       lowercase_   ,       lowercase_   )

       a_						     =			np.load(lowercase_   )
       a_						     =			stats[0].reshape(-1   )
       a_						     =			stats[1].reshape(-1   )
       a_						     =			torch.from_numpy(lowercase_   ).float()
       a_						     =			torch.from_numpy(lowercase_   ).float()

       model.save_pretrained(lowercase_   )

       if repo_id:
              print('Pushing to the hub...'   )
              model.push_to_hub(lowercase_   )


if __name__ == "__main__":
  __lowerCAmelCase        =							argparse.ArgumentParser()
  parser.add_argument("--checkpoint_path", required=True, default=None, type=str, help="Path to original checkpoint")
  parser.add_argument("--stats_path", required=True, default=None, type=str, help="Path to stats.npy file")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  parser.add_argument(
      "--pytorch_dump_folder_path", required=True, default=None, type=str, help="Path to the output PyTorch model."
  )
  parser.add_argument(
      "--push_to_hub", default=None, type=str, help="Where to upload the converted model on the 🤗 hub."
  )

  __lowerCAmelCase        =							parser.parse_args()
  convert_hifigan_checkpoint(
      args.checkpoint_path,
      args.stats_path,
      args.pytorch_dump_folder_path,
      args.config_path,
      args.push_to_hub,
  )




